– Case Study –
Global Financial Services Firm Builds AI Capability for Risk Assessment
Global Financial Services Firm requires the annotation of technical financial documents to train its AI platform to conduct risk assessments for investment portfolios
A leading global financial services company needed datasets to train its AI platform for investment risk assessment and analytics. The goal was for the company’s AI platform to be able to analyze textual information and determine the existence of risk, based on events, named individuals, and companies within the portfolio. It then would provide an assessment of the types and severity of the associated risk.
Combining Humans-in-the-loop & ML-Enabled Technology
Innodata’s team of subject matter experts created a taxonomy focused on model-relevant risk categories and risk stages. To bolster speed and ensure high-quality annotations throughout the articles, Innodata employed a combination of humans-in-the-loop and ML-enhanced technology. The articles were first run through Innodata’s proprietary text annotation platform, which completed an auto annotation. Then experts did a round of annotations to ensure accuracy and reviewed any low confidence annotations. Finally, our quality assurance specialist reviewed and resolved any discrepancies. The platform and annotators labeled the risks associated with events, named individuals, and companies within each article. They then identified risks within each article and assigned a risk category and level based on the agreed upon taxonomy.
The leading global financial services company’s risk assessment platform received a large annotated dataset of the highest quality based on thousands of relevant articles. This pristine data along with the risk taxonomy provided helped train and improve the model performance.
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